Hands-on Exercise 3.2

This is the Hands-on Exercise 3.2

Getting Started

• plotly is the R library for plotting interactive statistical graphs.
• gganimate is a ggplot extension for creating animated stastical graphs.
• gifski converts video frames to GIF animations using pngquant’s fancy features for efficient cross-frame palettes and temporal dithering. It produces animated GIFs that use thousands of colors per frame.
• gapminder is an excerpt of the data available at Gapminder.org. We just want to use its country_colors scheme.

Loading Packages

pacman::p_load(readxl, gifski, gapminder,
               plotly, gganimate, tidyverse)

Importing Data

col <- c("Country", "Continent")
globalPop <- read_xls("data/GlobalPopulation.xls",sheet="Data") %>%
  mutate_at(col, as.factor) %>%
  mutate(Year = as.integer(Year))

Animated Data Visualisation: gganimate

• transition_*() defines how the data should be spread out and how it relates to itself across time.
• view_*() defines how the positional scales should change along the animation.
• shadow_*() defines how data from other points in time should be presented in the given point in time.
• enter_()/exit_() defines how new data should appear and how old data should disappear during the course of the animation.
• ease_aes() defines how different aesthetics should be eased during transitions.

Building a static population bubble plot

ggplot(globalPop, aes(x = Old, y = Young, size = Population, colour = Country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', x = '% Aged', y = '% Young') 

Building the animated bubble plot

ggplot(globalPop, aes(x = Old, y = Young, size = Population, colour = Country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', x = '% Aged', y = '% Young') +
  transition_time(Year) +
  ease_aes('linear')

Using other gganimate

ggplot(globalPop, aes(x = Old, y = Young, size = Population, colour = Country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', x = '% Aged', y = '% Young') +
  transition_time(Year) +
view_follow(fixed_x = TRUE, fixed_y = TRUE) +
shadow_trail(0.1) +
enter_grow() +
exit_shrink() +
ease_aes("sine-in-out")

Animated Data Visualisation: plotly

Building an animated bubble plot: ggplotly() method

gg <- ggplot(globalPop, 
       aes(x = Old,y = Young, size = Population, colour = Country)) +
  geom_point(aes(size = Population, frame = Year), alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', y = '% Young')

ggplotly(gg)

gg <- ggplot(globalPop, 
       aes(x = Old,y = Young, size = Population, colour = Country)) +
  geom_point(aes(size = Population, frame = Year), alpha = 0.7) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', y = '% Young') + 
  theme(legend.position = 'none')

ggplotly(gg)

Building an animated bubble plot: plot_ly() method

bp <- globalPop %>%
  plot_ly(x = ~Old, y = ~Young, size = ~Population, color = ~Continent, sizes = c(2, 100), frame = ~Year, text = ~Country, hoverinfo = "text", type = 'scatter', mode = 'markers') %>%
  
  layout(showlegend = FALSE)
bp